{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import cv2\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "from torchvision import transforms,models\n",
    "from PIL import Image\n",
    "from keras.datasets import mnist\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = mnist.load_data()\n",
    "train_x, train_y, test_x, test_y = train[0], train[1], test[0], test[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(train_x[100])\n",
    "plt.title(train_y[100])\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(test_x[100])\n",
    "plt.title(test_y[100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fork from：https://github.com/adambielski/siamese-triplet/blob/master/Experiments_MNIST.ipynb\n",
    "\n",
    "mnist_classes = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',\n",
    "              '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n",
    "              '#bcbd22', '#17becf']\n",
    "\n",
    "def plot_embeddings(embeddings, targets, xlim=None, ylim=None):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for i in range(10):\n",
    "        inds = np.where(targets==i)[0]\n",
    "        plt.scatter(embeddings[inds,0], embeddings[inds,1], alpha=0.5, color=colors[i])\n",
    "    if xlim:\n",
    "        plt.xlim(xlim[0], xlim[1])\n",
    "    if ylim:\n",
    "        plt.ylim(ylim[0], ylim[1])\n",
    "    plt.legend(mnist_classes)\n",
    "\n",
    "def extract_embeddings(dataloader, model):\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        # embeddings = np.zeros((len(dataloader), 2))\n",
    "        # labels = np.zeros(len(dataloader))\n",
    "        k = 0\n",
    "        for i, (images, target) in enumerate(dataloader):\n",
    "            images = images.to(device)\n",
    "            output = model.get_embedding(images).data.cpu().numpy()\n",
    "            if i == 0:\n",
    "                embeddings = output\n",
    "                labels = target.numpy()\n",
    "            else:\n",
    "                embeddings = np.vstack((embeddings, output))\n",
    "                labels = np.hstack((labels, target))\n",
    "            embeddings[k:k+images.shape[0]] = output\n",
    "            labels[k:k+images.shape[0]] = target.numpy()\n",
    "            \n",
    "            k += images.shape[0]\n",
    "    return embeddings, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# part fork from：https://github.com/adambielski/siamese-triplet/blob/master/networks.py\n",
    "class EmbeddingNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(EmbeddingNet, self).__init__()\n",
    "        self.convnet = nn.Sequential(nn.Conv2d(1, 32, 5), nn.PReLU(),\n",
    "                                     nn.MaxPool2d(2, stride=2),\n",
    "                                     nn.Conv2d(32, 64, 5), nn.PReLU(),\n",
    "                                     nn.MaxPool2d(2, stride=2))\n",
    "\n",
    "        self.fc = nn.Sequential(nn.Linear(64 * 4 * 4, 256),\n",
    "                                nn.PReLU(),\n",
    "                                nn.Linear(256, 256),\n",
    "                                nn.PReLU(),\n",
    "                                nn.Linear(256, 2)\n",
    "                                )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.convnet(x)\n",
    "        output = output.view(output.size()[0], -1)\n",
    "        output = self.fc(output)\n",
    "        return output\n",
    "    \n",
    "    def get_embedding(self, x):\n",
    "        return self.forward(x)\n",
    "    \n",
    "class EmbeddingNetL2(EmbeddingNet):\n",
    "    def __init__(self):\n",
    "        super(EmbeddingNetL2, self).__init__()\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = super(EmbeddingNetL2, self).forward(x)\n",
    "        output /= output.pow(2).sum(1, keepdim=True).sqrt()\n",
    "        return output\n",
    "\n",
    "    def get_embedding(self, x):\n",
    "        return self.forward(x)\n",
    "    \n",
    "    \n",
    "    \n",
    "class ClassifierNet(nn.Module):\n",
    "    def __init__(self, categories):\n",
    "        super(ClassifierNet, self).__init__()\n",
    "        self.embeddingnet = EmbeddingNet()\n",
    "        self.nonlinear = nn.PReLU()\n",
    "        self.fc = nn.Linear(2, categories)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.embeddingnet(x)\n",
    "        x = self.nonlinear(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "    \n",
    "    def get_embedding(self, x):\n",
    "        return self.nonlinear(self.embeddingnet(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataloader for softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGen(Dataset):\n",
    "    def __init__(self, data, label, transforms=None):\n",
    "        self.data = data\n",
    "        self.label = label\n",
    "        self.transforms = transforms\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        img = self.data[index]\n",
    "        tag = self.label[index]\n",
    "        if self.transforms:\n",
    "            img = Image.fromarray(img.astype('uint8'))\n",
    "            img = self.transforms(img)\n",
    "        return img, tag\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "traingen = DataGen(train_x, train_y, transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "trainloader = DataLoader(dataset=traingen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)\n",
    "testgen = DataGen(test_x, test_y, transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "testloader = DataLoader(dataset=testgen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 28, 28]) tensor([ 8,  6,  2,  9,  9,  4,  4,  1,  5,  8,  6,  2,  1,  7,\n",
      "         3,  6,  4,  1,  4,  3,  7,  2,  9,  3,  0,  7,  1,  1,\n",
      "         3,  3,  0,  2], dtype=torch.uint8)\n"
     ]
    }
   ],
   "source": [
    "temp = iter(trainloader)\n",
    "img, label = next(temp)\n",
    "print(img.shape,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ClassifierNet(10)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/10, steps:1, loss:2.3157362937927246\n",
      "1/10, steps:101, loss:1.3582979440689087\n",
      "1/10, steps:201, loss:1.009324312210083\n",
      "1/10, steps:301, loss:0.7351540923118591\n",
      "1/10, steps:401, loss:0.609061598777771\n",
      "1/10, steps:501, loss:0.42083364725112915\n",
      "1/10, steps:601, loss:0.49983254075050354\n",
      "1/10, steps:701, loss:0.611308217048645\n",
      "1/10, steps:801, loss:0.23569631576538086\n",
      "1/10, steps:901, loss:0.27480119466781616\n",
      "1/10, steps:1001, loss:0.11253610253334045\n",
      "1/10, steps:1101, loss:0.1295250803232193\n",
      "1/10, steps:1201, loss:0.04506021738052368\n",
      "1/10, steps:1301, loss:0.5298935174942017\n",
      "1/10, steps:1401, loss:0.09185680001974106\n",
      "1/10, steps:1501, loss:0.2397640198469162\n",
      "1/10, steps:1601, loss:0.1332893818616867\n",
      "1/10, steps:1701, loss:0.06380537152290344\n",
      "1/10, steps:1801, loss:0.02346399426460266\n",
      "Average Accuracy is 0.9777644230769231\n",
      "2/10, steps:1, loss:0.04885256290435791\n",
      "2/10, steps:101, loss:0.08184562623500824\n",
      "2/10, steps:201, loss:0.01470184326171875\n",
      "2/10, steps:301, loss:0.10408447682857513\n",
      "2/10, steps:401, loss:0.07097208499908447\n",
      "2/10, steps:501, loss:0.05742645263671875\n",
      "2/10, steps:601, loss:0.04746130108833313\n",
      "2/10, steps:701, loss:0.027025818824768066\n",
      "2/10, steps:801, loss:0.03746332228183746\n",
      "2/10, steps:901, loss:0.07080423831939697\n",
      "2/10, steps:1001, loss:0.12174802273511887\n",
      "2/10, steps:1101, loss:0.05388571321964264\n",
      "2/10, steps:1201, loss:0.15246741473674774\n",
      "2/10, steps:1301, loss:0.23135271668434143\n",
      "2/10, steps:1401, loss:0.0292813777923584\n",
      "2/10, steps:1501, loss:0.02725560963153839\n",
      "2/10, steps:1601, loss:0.029880791902542114\n",
      "2/10, steps:1701, loss:0.05335060507059097\n",
      "2/10, steps:1801, loss:0.15606971085071564\n",
      "Average Accuracy is 0.9834735576923077\n",
      "3/10, steps:1, loss:0.09071094542741776\n",
      "3/10, steps:101, loss:0.09831810742616653\n",
      "3/10, steps:201, loss:1.0574944019317627\n",
      "3/10, steps:301, loss:0.08201578259468079\n",
      "3/10, steps:401, loss:0.020958364009857178\n",
      "3/10, steps:501, loss:0.0019551515579223633\n",
      "3/10, steps:601, loss:0.08267519623041153\n",
      "3/10, steps:701, loss:0.0030077099800109863\n",
      "3/10, steps:801, loss:0.0320536270737648\n",
      "3/10, steps:901, loss:0.14779435098171234\n",
      "3/10, steps:1001, loss:0.004154026508331299\n",
      "3/10, steps:1101, loss:0.1671464890241623\n",
      "3/10, steps:1201, loss:0.036309272050857544\n",
      "3/10, steps:1301, loss:0.07800790667533875\n",
      "3/10, steps:1401, loss:0.045653775334358215\n",
      "3/10, steps:1501, loss:0.02288830280303955\n",
      "3/10, steps:1601, loss:0.007956922054290771\n",
      "3/10, steps:1701, loss:0.05151980370283127\n",
      "3/10, steps:1801, loss:0.026522278785705566\n",
      "Average Accuracy is 0.9842748397435898\n",
      "4/10, steps:1, loss:0.22917217016220093\n",
      "4/10, steps:101, loss:0.044956862926483154\n",
      "4/10, steps:201, loss:0.0710119754076004\n",
      "4/10, steps:301, loss:0.021795794367790222\n",
      "4/10, steps:401, loss:0.026797890663146973\n",
      "4/10, steps:501, loss:0.0038917064666748047\n",
      "4/10, steps:601, loss:0.0026638805866241455\n",
      "4/10, steps:701, loss:0.02521541714668274\n",
      "4/10, steps:801, loss:0.011116981506347656\n",
      "4/10, steps:901, loss:0.0032861828804016113\n",
      "4/10, steps:1001, loss:0.026722252368927002\n",
      "4/10, steps:1101, loss:0.001825779676437378\n",
      "4/10, steps:1201, loss:0.08824151754379272\n",
      "4/10, steps:1301, loss:0.042718276381492615\n",
      "4/10, steps:1401, loss:0.0011272728443145752\n",
      "4/10, steps:1501, loss:0.026314765214920044\n",
      "4/10, steps:1601, loss:0.2021709680557251\n",
      "4/10, steps:1701, loss:0.09183590859174728\n",
      "4/10, steps:1801, loss:0.0019993185997009277\n",
      "Average Accuracy is 0.9845753205128205\n",
      "5/10, steps:1, loss:0.03146904706954956\n",
      "5/10, steps:101, loss:0.009205222129821777\n",
      "5/10, steps:201, loss:0.0008829236030578613\n",
      "5/10, steps:301, loss:0.0032866299152374268\n",
      "5/10, steps:401, loss:0.015130043029785156\n",
      "5/10, steps:501, loss:0.17186957597732544\n",
      "5/10, steps:601, loss:0.0447632372379303\n",
      "5/10, steps:701, loss:0.19614414870738983\n",
      "5/10, steps:801, loss:0.003250598907470703\n",
      "5/10, steps:901, loss:0.016081571578979492\n",
      "5/10, steps:1001, loss:0.012606054544448853\n",
      "5/10, steps:1101, loss:0.0272333025932312\n",
      "5/10, steps:1201, loss:0.0031952857971191406\n",
      "5/10, steps:1301, loss:0.002120494842529297\n",
      "5/10, steps:1401, loss:0.15269529819488525\n",
      "5/10, steps:1501, loss:0.016433000564575195\n",
      "5/10, steps:1601, loss:0.0036456286907196045\n",
      "5/10, steps:1701, loss:0.09263475984334946\n",
      "5/10, steps:1801, loss:0.0225105881690979\n",
      "Average Accuracy is 0.9864783653846154\n",
      "6/10, steps:1, loss:0.0022632479667663574\n",
      "6/10, steps:101, loss:0.011632204055786133\n",
      "6/10, steps:201, loss:0.0030152499675750732\n",
      "6/10, steps:301, loss:0.003917902708053589\n",
      "6/10, steps:401, loss:0.04985088109970093\n",
      "6/10, steps:501, loss:0.062243521213531494\n",
      "6/10, steps:601, loss:0.01357424259185791\n",
      "6/10, steps:701, loss:0.07019498944282532\n",
      "6/10, steps:801, loss:0.006569594144821167\n",
      "6/10, steps:901, loss:0.008481532335281372\n",
      "6/10, steps:1001, loss:0.02521967887878418\n",
      "6/10, steps:1101, loss:0.017816007137298584\n",
      "6/10, steps:1201, loss:0.0005024075508117676\n",
      "6/10, steps:1301, loss:0.09615413099527359\n",
      "6/10, steps:1401, loss:0.016445279121398926\n",
      "6/10, steps:1501, loss:0.011515751481056213\n",
      "6/10, steps:1601, loss:0.0019452273845672607\n",
      "6/10, steps:1701, loss:0.006118953227996826\n",
      "6/10, steps:1801, loss:0.05191659927368164\n",
      "Average Accuracy is 0.9893830128205128\n",
      "7/10, steps:1, loss:0.005447328090667725\n",
      "7/10, steps:101, loss:0.0002211928367614746\n",
      "7/10, steps:201, loss:0.0045452117919921875\n",
      "7/10, steps:301, loss:0.0029587745666503906\n",
      "7/10, steps:401, loss:0.04949308931827545\n",
      "7/10, steps:501, loss:0.11374092102050781\n",
      "7/10, steps:601, loss:0.5132740139961243\n",
      "7/10, steps:701, loss:0.02633519470691681\n",
      "7/10, steps:801, loss:0.02974081039428711\n",
      "7/10, steps:901, loss:0.05372989922761917\n",
      "7/10, steps:1001, loss:0.0008647441864013672\n",
      "7/10, steps:1101, loss:0.06475532799959183\n",
      "7/10, steps:1201, loss:0.007171899080276489\n",
      "7/10, steps:1301, loss:0.00010484457015991211\n",
      "7/10, steps:1401, loss:0.019074976444244385\n",
      "7/10, steps:1501, loss:0.183487206697464\n",
      "7/10, steps:1601, loss:0.07004069536924362\n",
      "7/10, steps:1701, loss:0.1123569905757904\n",
      "7/10, steps:1801, loss:0.0023426711559295654\n",
      "Average Accuracy is 0.9858774038461539\n",
      "8/10, steps:1, loss:0.0071891844272613525\n",
      "8/10, steps:101, loss:0.0006459355354309082\n",
      "8/10, steps:201, loss:0.0036962032318115234\n",
      "8/10, steps:301, loss:0.3658845126628876\n",
      "8/10, steps:401, loss:0.02471911907196045\n",
      "8/10, steps:501, loss:0.0008843839168548584\n",
      "8/10, steps:601, loss:0.01371869444847107\n",
      "8/10, steps:701, loss:0.004221320152282715\n",
      "8/10, steps:801, loss:0.0023999810218811035\n",
      "8/10, steps:901, loss:0.03045007586479187\n",
      "8/10, steps:1001, loss:0.00985807180404663\n",
      "8/10, steps:1101, loss:0.0003905296325683594\n",
      "8/10, steps:1201, loss:0.0012728869915008545\n",
      "8/10, steps:1301, loss:0.009439796209335327\n",
      "8/10, steps:1401, loss:0.05196850001811981\n",
      "8/10, steps:1501, loss:0.0005105733871459961\n",
      "8/10, steps:1601, loss:0.008716583251953125\n",
      "8/10, steps:1701, loss:0.3246879577636719\n",
      "8/10, steps:1801, loss:0.0027028918266296387\n",
      "Average Accuracy is 0.98828125\n",
      "9/10, steps:1, loss:0.0038961470127105713\n",
      "9/10, steps:101, loss:0.0003514885902404785\n",
      "9/10, steps:201, loss:0.012555539608001709\n",
      "9/10, steps:301, loss:0.0037114620208740234\n",
      "9/10, steps:401, loss:0.04760795831680298\n",
      "9/10, steps:501, loss:0.04331047087907791\n",
      "9/10, steps:601, loss:0.006455749273300171\n",
      "9/10, steps:701, loss:0.07053165882825851\n",
      "9/10, steps:801, loss:0.07695283740758896\n",
      "9/10, steps:901, loss:0.007613644003868103\n",
      "9/10, steps:1001, loss:0.0006801486015319824\n",
      "9/10, steps:1101, loss:0.17026911675930023\n",
      "9/10, steps:1201, loss:0.05612766742706299\n",
      "9/10, steps:1301, loss:0.009623974561691284\n",
      "9/10, steps:1401, loss:0.0026317834854125977\n",
      "9/10, steps:1501, loss:0.006795436143875122\n",
      "9/10, steps:1601, loss:0.026238977909088135\n",
      "9/10, steps:1701, loss:0.002890646457672119\n",
      "9/10, steps:1801, loss:0.02862665057182312\n",
      "Average Accuracy is 0.9888822115384616\n",
      "10/10, steps:1, loss:0.013079121708869934\n",
      "10/10, steps:101, loss:0.004853188991546631\n",
      "10/10, steps:201, loss:0.13200941681861877\n",
      "10/10, steps:301, loss:0.007006198167800903\n",
      "10/10, steps:401, loss:0.030262529850006104\n",
      "10/10, steps:501, loss:0.00038182735443115234\n",
      "10/10, steps:601, loss:0.027675360441207886\n",
      "10/10, steps:701, loss:0.00019979476928710938\n",
      "10/10, steps:801, loss:0.20173877477645874\n",
      "10/10, steps:901, loss:0.04704777151346207\n",
      "10/10, steps:1001, loss:0.001827538013458252\n",
      "10/10, steps:1101, loss:4.70280647277832e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10, steps:1201, loss:0.004020228981971741\n",
      "10/10, steps:1301, loss:0.0013758540153503418\n",
      "10/10, steps:1401, loss:0.0011112689971923828\n",
      "10/10, steps:1501, loss:0.003965109586715698\n",
      "10/10, steps:1601, loss:0.07080674171447754\n",
      "10/10, steps:1701, loss:0.018589168787002563\n",
      "10/10, steps:1801, loss:0.000330507755279541\n",
      "Average Accuracy is 0.9872796474358975\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "\n",
    "model = model.to(device)\n",
    "for i in range(epochs):\n",
    "    for j, (img, tag) in enumerate(trainloader):\n",
    "        img = img.to(device)\n",
    "        tag = tag.to(device)\n",
    "        # print(tag)\n",
    "        output = model(img)\n",
    "        loss = criterion(output, tag.type(torch.cuda.LongTensor))\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if j % 100 == 0:\n",
    "            print('{}/{}, steps:{}, loss:{}'.format(i+1, epochs, j+1, loss.item()))\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        for img, tag in testloader:\n",
    "            img = img.to(device)\n",
    "            tag = tag.to(device).type(torch.cuda.LongTensor)\n",
    "            output = model(img)\n",
    "            _, predicted = torch.max(output.data, 1)\n",
    "            correct += (predicted == tag).sum().item()\n",
    "            total += tag.shape[0]\n",
    "        print('Average Accuracy is {}'.format(correct / total))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化softmax结果\n",
    "可视化基于分类的特征空间分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emebddings, labels = extract_embeddings(dataloader=testloader, model=model)\n",
    "plot_embeddings(emebddings, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataloder for Siamese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Siamese翻译过来是孪生，也是说两个一摸一样的模型\n",
    "实际上只有一个模型，只是成对的数据通过它能够判断是否属于同一类别\n",
    "所以这里需要编写孪生网络所需要的数据生成器，同类赋予label：1，异类赋予label：0\n",
    "训练：\n",
    "是否是同类，靠随机选取0/1值查看，\n",
    "'''\n",
    "import random\n",
    "class SiameseDataset(Dataset):\n",
    "    def __init__(self, images, labels, tag='train', transforms=None):\n",
    "        self.data = images\n",
    "        self.labels = labels\n",
    "        self.tag = tag\n",
    "        self.transforms = transforms\n",
    "        self.label_set = set(labels)\n",
    "        self.label2index = { i:list(np.where(labels==i)[0]) for i in self.label_set}\n",
    "        \n",
    "        if tag != 'train':\n",
    "            '''\n",
    "            依次遍历整个数据集，第i个数据，作为它的正pair，随机从所有该类别内选取一个。\n",
    "            '''\n",
    "            positive_pairs = [[i, \n",
    "                               random.choice(self.label2index[self.labels[i]]), \n",
    "                               1] \n",
    "                              for i in range(self.data.shape[0])]\n",
    "            \n",
    "            '''\n",
    "            依次遍历整个数据集，第i个数据，作为它的负pair，随机从剔除该类后的其他类中随机选取一个类，然后再从选取的类中\n",
    "            随机选取一个数据\n",
    "            '''\n",
    "            negative_pairs = [[i, \n",
    "                               random.choice(self.label2index[random.choice(list(self.label_set - set([self.labels[i]])))]), \n",
    "                               0] \n",
    "                              for i in range(self.data.shape[0])]\n",
    "            \n",
    "            self.test_paires = positive_pairs + negative_pairs\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        if self.tag == 'train':\n",
    "            img1 = self.data[index]\n",
    "            label = self.labels[index]\n",
    "            siamese_nor = random.randint(0,1)\n",
    "            if siamese_nor == 1:\n",
    "                siamese_index = index\n",
    "                \n",
    "                while siamese_index == index:\n",
    "                    siamese_index = random.choice(self.label2index[label])\n",
    "            else:\n",
    "                siamese_label = random.choice(list(self.label_set - set([label])))\n",
    "                siamese_index = random.choice(self.label2index[siamese_label])\n",
    "            img2 = self.data[siamese_index]\n",
    "            label = siamese_nor\n",
    "            \n",
    "        else:\n",
    "            sample = self.test_paires[index]\n",
    "            img1 = self.data[sample[0]]\n",
    "            img2 = self.data[sample[1]]\n",
    "            label = sample[2]\n",
    "        \n",
    "        if self.transforms:\n",
    "            img1 = self.transforms(Image.fromarray(img1))\n",
    "            img2 = self.transforms(Image.fromarray(img2))\n",
    "\n",
    "            \n",
    "        return img1, img2, label\n",
    "    \n",
    "    def __len__(self):\n",
    "        if self.tag == 'train':\n",
    "            return len(self.data)\n",
    "        else:\n",
    "            return len(self.test_paires)\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "traingen = SiameseDataset(train_x, train_y, tag='train', transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "trainloader = DataLoader(dataset=traingen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)\n",
    "testgen = SiameseDataset(test_x, test_y, tag='test', transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "testloader = DataLoader(dataset=testgen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检查生成器\n",
    "查看下生成器是否正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 28, 28]) torch.Size([32, 1, 28, 28]) tensor([ 0,  1,  1,  0,  0,  1,  1,  1,  0,  0,  0,  1,  1,  0,\n",
      "         0,  1,  1,  1,  1,  0,  1,  1,  1,  1,  0,  1,  0,  1,\n",
      "         1,  0,  0,  0])\n"
     ]
    }
   ],
   "source": [
    "temp = iter(trainloader)\n",
    "img1, img2, label = next(temp)\n",
    "print(img1.shape,img2.shape,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(img1[2][0].numpy())\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(img2[2][0].numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对比损失\n",
    "开始编写对比损失，用于训练\n",
    "\\begin{equation}\n",
    "ContrastiveLoss = \\frac{1}{2}\\sum y\\ast d^{2} + \\frac{1}{2}\\sum \\left ( 1-y \\right )\\ast (ReLu( margin -  d))^{2}\n",
    "\\label{eq:ContrastiveLoss}\n",
    "\\end{equation}\n",
    "其中d表示两个样本在空间上的欧式距离\n",
    "\\begin{equation}\n",
    "d = \\left \\| embedding_{1} - embedding_{2} \\right \\|\n",
    "\\label{eq:Euclidean distance}\n",
    "\\end{equation}\n",
    "\n",
    "从公式上可以看出，当y=1，也就是两个样本属于同类的时候，loss=y*d，这样迫使网络降低d的大小，当y=0，两个样本不属于同类，那么迫使d忘margin靠近，也就是距离尽量远，最终得到的结果就是类内距小（类内方差小，形成一个密集的簇），类间距趋于为margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ContrastiveLoss(nn.Module):\n",
    "    def __init__(self, margin):\n",
    "        super(ContrastiveLoss, self).__init__()\n",
    "        self.margin = margin\n",
    "        \n",
    "    def forward(self, embed1, embed2, label):\n",
    "        Euclid_dis = (embed1 - embed2).pow(2).sum(1)\n",
    "        loss = 0.5 * (label * Euclid_dis) + 0.5 * (1 - label) * F.relu(self.margin - Euclid_dis).pow(2)\n",
    "        \n",
    "        return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = EmbeddingNet()\n",
    "criterion = ContrastiveLoss(margin=1)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:13<00:00, 142.83it/s]\n",
      "  0%|          | 0/625 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "1/10, loss:0.045292938274641835\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 329.25it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "1/10, loss:0.016553392502292992\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:12<00:00, 147.90it/s]\n",
      "  0%|          | 0/625 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "2/10, loss:0.013336587599851192\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 340.42it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "2/10, loss:0.009065443953126669\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:11<00:00, 163.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "3/10, loss:0.008518986386666075\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 320.37it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "3/10, loss:0.00912322435383685\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:12<00:00, 155.71it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "4/10, loss:0.005924561382022997\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:02<00:00, 309.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "4/10, loss:0.009019613889814355\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:12<00:00, 148.48it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "5/10, loss:0.0038454651392957504\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 327.93it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "5/10, loss:0.006132525706739398\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:11<00:00, 159.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "6/10, loss:0.004060930869150131\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 337.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "6/10, loss:0.006851168476126622\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:11<00:00, 163.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "7/10, loss:0.003111215924359082\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 321.40it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "7/10, loss:0.006851974920136854\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:12<00:00, 152.27it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "8/10, loss:0.002197194362373557\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:02<00:00, 305.91it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "8/10, loss:0.00843005817963858\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:11<00:00, 166.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "9/10, loss:0.002275183981853964\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 331.17it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "9/10, loss:0.006006137993838638\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:13<00:00, 144.23it/s]\n",
      "  0%|          | 0/625 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "10/10, loss:0.0019203477624085887\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 625/625 [00:01<00:00, 321.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "10/10, loss:0.0064664891421212815\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "model = model.to(device)\n",
    "for i in range(epochs):\n",
    "    total_loss = 0\n",
    "    for (img1, img2, tag) in tqdm(trainloader):\n",
    "        img1 = img1.to(device)\n",
    "        img2 = img2.to(device)\n",
    "        tag = tag.to(device).type(torch.cuda.FloatTensor)\n",
    "        # print(tag)\n",
    "        embedding1 = model(img1)\n",
    "        embedding2 = model(img2)\n",
    "        loss = criterion(embedding1, embedding2, tag)\n",
    "        total_loss += loss.item()\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    print('############## taining ##############')\n",
    "    print('{}/{}, loss:{}'.format(i+1, epochs, total_loss / len(trainloader)))\n",
    "    print('############## taining ##############')\n",
    "    \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        test_total_loss = 0\n",
    "        for (img1, img2, tag) in tqdm(testloader):\n",
    "            img1 = img1.to(device)\n",
    "            img2 = img2.to(device)\n",
    "            tag = tag.to(device).type(torch.cuda.FloatTensor)\n",
    "            embedding1 = model(img1)\n",
    "            embedding2 = model(img2)\n",
    "            loss = criterion(embedding1, embedding2, tag)\n",
    "            test_total_loss += loss.item()\n",
    "        print('############## testing ##############')\n",
    "        print('{}/{}, loss:{}'.format(i+1, epochs, test_total_loss / len(testloader)))\n",
    "        print('############## testing ##############')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 注意：这里调用下分类的生成器，因为主要是为了提取特征，孪生的生成器不适用这里\n",
    "emebddings, labels = extract_embeddings(dataloader=testloader, model=model)\n",
    "plot_embeddings(emebddings, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataloader for Triplet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Triplet翻译过来是三元组的意思，分别表示选中样本，一个正样本，一个负样本\n",
    "训练：\n",
    "同类的距离越近越好，异类的距离越远越好\n",
    "'''\n",
    "import random\n",
    "class TripletDataset(Dataset):\n",
    "    def __init__(self, images, labels, tag='train', transforms=None):\n",
    "        self.data = images\n",
    "        self.labels = labels\n",
    "        self.tag = tag\n",
    "        self.transforms = transforms\n",
    "        self.label_set = set(labels)\n",
    "        self.label2index = { i:list(np.where(labels==i)[0]) for i in self.label_set}\n",
    "        \n",
    "        if tag != 'train':\n",
    "            '''\n",
    "            依次遍历整个数据集，第i个数据，作为它的正pair，随机从所有该类别内选取一个，负样本随机选取一个异类，再随机选取其中一个样本。\n",
    "            '''\n",
    "            triplets = [[i, \n",
    "                        random.choice(self.label2index[self.labels[i]]), \n",
    "                        random.choice(self.label2index[random.choice(list(self.label_set - set([self.labels[i]])))])\n",
    "                        ] \n",
    "                        for i in range(self.data.shape[0])]\n",
    "            \n",
    "\n",
    "            \n",
    "            self.test_triplet = triplets\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        if self.tag == 'train':\n",
    "            img1 = self.data[index]\n",
    "            label = self.labels[index]\n",
    "            \n",
    "            postive_index = index\n",
    "            while postive_index == index:\n",
    "                postive_index = random.choice(self.label2index[label])\n",
    "            img2 = self.data[postive_index\n",
    "                            ]\n",
    "            negtive_label = random.choice(list(self.label_set - set([label])))\n",
    "            negtive_index = random.choice(self.label2index[negtive_label])\n",
    "            img3 = self.data[negtive_index]\n",
    "            \n",
    "        else:\n",
    "            sample = self.test_triplet[index]\n",
    "            img1 = self.data[sample[0]]\n",
    "            img2 = self.data[sample[1]]\n",
    "            img3 = self.data[sample[2]]\n",
    "        \n",
    "        if self.transforms:\n",
    "            img1 = self.transforms(Image.fromarray(img1))\n",
    "            img2 = self.transforms(Image.fromarray(img2))\n",
    "            img3 = self.transforms(Image.fromarray(img3))\n",
    "\n",
    "            \n",
    "        return img1, img2, img3\n",
    "    \n",
    "    def __len__(self):\n",
    "        if self.tag == 'train':\n",
    "            return len(self.data)\n",
    "        else:\n",
    "            return len(self.test_triplet)\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "traingen = TripletDataset(train_x, train_y, tag='train', transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "trainloader = DataLoader(dataset=traingen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)\n",
    "testgen = TripletDataset(test_x, test_y, tag='test', transforms=transforms.Compose([transforms.ToTensor()]))\n",
    "testloader = DataLoader(dataset=testgen,\n",
    "                         batch_size=32,\n",
    "                         pin_memory=False,\n",
    "                         drop_last=True,\n",
    "                         shuffle=True,\n",
    "                         num_workers=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 28, 28]) torch.Size([32, 1, 28, 28]) torch.Size([32, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "temp = iter(trainloader)\n",
    "img1, img2, img3 = next(temp)\n",
    "print(img1.shape, img2.shape, img3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TripletLoss\n",
    "开始编写triplet loss  \n",
    "\n",
    "\\begin{equation}\n",
    "TripletLoss = max(0, \\left \\| embedding_{anchor} - embedding_{postive} \\right \\|+ margin - \\left \\| embedding_{anchor} - embedding_{negtive} \\right \\|)\n",
    "\\label{eq:TripletLoss}\n",
    "\\end{equation}  \n",
    "\n",
    "该损失有三个输入，第一个anchor为锚点，postive为它的正样本，negtive为它的负样本，这个损失的思想就是锚点与负样本的距离相比与正样本的距离，始终都要大于/等于margin，它只在乎类间距，类内距不关心，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TripleLoss(nn.Module):\n",
    "    def __init__(self, margin):\n",
    "        super(TripleLoss, self).__init__()\n",
    "        self.margin = margin\n",
    "        \n",
    "    def forward(self, a, p, n):\n",
    "        ap = (a - p).pow(2).sum(1).sqrt()\n",
    "        an = (a - n).pow(2).sum(1).sqrt()\n",
    "        loss = F.relu(self.margin + ap - an)\n",
    "        return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = EmbeddingNet()\n",
    "criterion = TripleLoss(margin=1)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:14<00:00, 125.03it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "1/10, loss:0.12208410532176495\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 351.39it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "1/10, loss:0.27905179804955155\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:14<00:00, 125.35it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "2/10, loss:0.03900443653166294\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 347.08it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "2/10, loss:0.17890407632773694\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:14<00:00, 133.82it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "3/10, loss:0.02682849454085032\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 349.89it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "3/10, loss:0.13524155137248528\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:14<00:00, 129.51it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "4/10, loss:0.023869294673204422\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 326.87it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "4/10, loss:0.1075572249216911\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:13<00:00, 137.60it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "5/10, loss:0.020457658395171167\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 358.80it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "5/10, loss:0.09015880267804441\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:15<00:00, 122.28it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "6/10, loss:0.017754235817988715\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 319.50it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "6/10, loss:0.08647458577074875\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:16<00:00, 117.14it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "7/10, loss:0.014819378702839215\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 323.46it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "7/10, loss:0.07458421202877966\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:16<00:00, 113.46it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "8/10, loss:0.014051917548974354\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 333.16it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "8/10, loss:0.06982268569155191\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:15<00:00, 121.77it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "9/10, loss:0.012636105716228484\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 348.66it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "9/10, loss:0.10538623517809007\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:14<00:00, 125.98it/s]\n",
      "  0%|          | 0/1875 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## taining ##############\n",
      "10/10, loss:0.01255327506562074\n",
      "############## taining ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1875/1875 [00:05<00:00, 325.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## testing ##############\n",
      "10/10, loss:0.0712611548578701\n",
      "############## testing ##############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "model = model.to(device)\n",
    "for i in range(epochs):\n",
    "    total_loss = 0\n",
    "    for (img1, img2, img3) in tqdm(trainloader):\n",
    "        img1 = img1.to(device)\n",
    "        img2 = img2.to(device)\n",
    "        img3 = img3.to(device)\n",
    "        # print(tag)\n",
    "        a = model(img1)\n",
    "        p = model(img2)\n",
    "        n = model(img3)\n",
    "        loss = criterion(a, p, n)\n",
    "        total_loss += loss.item()\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "    print('############## taining ##############')\n",
    "    print('{}/{}, loss:{}'.format(i+1, epochs, total_loss / len(trainloader)))\n",
    "    print('############## taining ##############')\n",
    "    \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        test_total_loss = 0\n",
    "        for (img1, img2, img3) in tqdm(trainloader):\n",
    "            img1 = img1.to(device)\n",
    "            img2 = img2.to(device)\n",
    "            img3 = img3.to(device)\n",
    "            # print(tag)\n",
    "            a = model(img1)\n",
    "            p = model(img2)\n",
    "            n = model(img3)\n",
    "            loss = criterion(a, p, n)\n",
    "            test_total_loss += loss.item()\n",
    "        print('############## testing ##############')\n",
    "        print('{}/{}, loss:{}'.format(i+1, epochs, test_total_loss / len(testloader)))\n",
    "        print('############## testing ##############')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embeddings, labels = extract_embeddings(testloader, model)\n",
    "plot_embeddings(embeddings, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
